the aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an espec...
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ISBN:
(纸本)9783642341236
the aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. the commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones.
Sample-then-lock construction is a reusable fuzzy extractor for low-entropy sources. When applied on iris recognition scenarios, many subsets of an iris-code are used to lock the cryptographic key. the security of thi...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Sample-then-lock construction is a reusable fuzzy extractor for low-entropy sources. When applied on iris recognition scenarios, many subsets of an iris-code are used to lock the cryptographic key. the security of this construction relies on the entropy of subsets of iris codes. Simhadri et al. reported a security level of 32 bits on iris sources. In this paper, we propose two kinds of attacks to crack existing sample-then-lock schemes. Exploiting the low-entropy subsets, our attacks can break the locked key and the enrollment iris-code respectively in less than 2(20) brute force attempts. To protect from these proposed attacks, we design an improved sample-then-lock scheme. More precisely, our scheme employs stability and discriminability to select high-entropy subsets to lock the genuine secret, and conceals genuine locker by a large amount of chaff lockers. Our experiment verifies that existing schemes are vulnerable to the proposed attacks with a security level of less than 20 bits, while our scheme can resist these attacks with a security level of more than 100 bits when number of genuine subsets is 10(6).
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